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"Suzuki, Hideaki"
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Prediction of the development of delirium after transcatheter aortic valve implantation using preoperative brain perfusion SPECT
Delirium is an important prognostic factor in postoperative patients undergoing cardiovascular surgery and intervention, including transcatheter aortic valve implantation (TAVI). However, delirium after transcatheter aortic valve implantation (DAT) is difficult to predict and its pathophysiology is still unclear. We aimed to investigate whether preoperative cerebral blood flow (CBF) is associated with DAT and, if so, whether CBF measurement is useful for predicting DAT. We evaluated CBF in 50 consecutive patients before TAVI (84.7±4.5 yrs., 36 females) using .sup.99m Tc ethyl cysteinate dimer single-photon emission computed tomography. Preoperative CBF of the DAT group (N = 12) was compared with that of the non-DAT group (N = 38) using whole brain voxel-wise analysis with SPM12 and region of interest-based analysis with the easy-Z score imaging system. Multivariable logistic regression analysis with the presence of DAT was used to create its prediction model. The whole brain analysis showed that preoperative CBF in the insula was lower in the DAT than in the non-DAT group (P<0.05, family-wise error correction). Decrease extent ratio in the insula of the DAT group (17.6±11.5%) was also greater relative to that of the non-DAT group (7.0±11.3%) in the region of interest-based analysis (P = 0.007). A model that included preoperative CBF in the insula and conventional indicators (frailty index, short physical performance battery and mini-mental state examination) showed the best predictive power for DAT (AUC 0.882). These results suggest that preoperative CBF in the insula is associated with DAT and may be useful for its prediction.
Journal Article
A population-based phenome-wide association study of cardiac and aortic structure and function
Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.
Using magnetic resonance images of the heart and aorta from 26,893 individuals in the UK Biobank, a phenome-wide association study associates cardiovascular imaging phenotypes with a wide range of demographic, lifestyle and clinical features.
Journal Article
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
Background
Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images.
Methods
Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV).
Results
By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images. On a short-axis image test set of 600 subjects, it achieves an average Dice metric of 0.94 for the LV cavity, 0.88 for the LV myocardium and 0.90 for the RV cavity. The mean absolute difference between automated measurement and manual measurement is 6.1 mL for LVEDV, 5.3 mL for LVESV, 6.9 gram for LVM, 8.5 mL for RVEDV and 7.2 mL for RVESV. On long-axis image test sets, the average Dice metric is 0.93 for the LA cavity (2-chamber view), 0.95 for the LA cavity (4-chamber view) and 0.96 for the RA cavity (4-chamber view). The performance is comparable to human inter-observer variability.
Conclusions
We show that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinically relevant measures.
Journal Article
Relevant Obstetric Factors for Cerebral Palsy: From the Nationwide Obstetric Compensation System in Japan
Objective\\nThe aim of this study was to identify the relevant obstetric factors for cerebral palsy (CP) after 33 weeks' gestation in Japan.\\n\\nStudy design\\nThis retrospective case cohort study (1:100 cases and controls) used a Japanese national CP registry. Obstetric characteristics and clinical course were compared between CP cases in the Japan Obstetric Compensation System for Cerebral Palsy database and controls in the perinatal database of the Japan Society of Obstetrics and Gynecology born as live singleton infants between 2009 and 2011 with a birth weight ≥ 2,000 g and gestation ≥ 33 weeks.\\n\\nResults\\nOne hundred and seventy-five CP cases and 17,475 controls were assessed. Major relevant single factors for CP were placental abnormalities (31%), umbilical cord abnormalities (15%), maternal complications (10%), and neonatal complications (1%). A multivariate regression model demonstrated that obstetric variables associated with CP were acute delivery due to non-reassuring fetal status (relative risk [RR]: 37.182, 95% confidence interval [CI]: 20.028–69.032), uterine rupture (RR: 24.770, 95% CI: 6.006–102.160), placental abruption (RR: 20.891, 95% CI: 11.817–36.934), and preterm labor (RR: 3.153, 95% CI: 2.024–4.911), whereas protective factors were head presentation (RR: 0.199, 95% CI: 0.088–0.450) and elective cesarean section (RR: 0.236, 95% CI: 0.067–0.828).\\n\\nConclusion\\nCP after 33 weeks' gestation in the recently reported cases in Japan was strongly associated with acute delivery due to non-reassuring fetal status, uterine rupture, and placental abruption.
Journal Article
NKX2-1 re-expression induces cell death through apoptosis and necrosis in dedifferentiated thyroid carcinoma cells
NK2 homeobox 1 (NKX2-1) is a thyroid transcription factor essential for proper thyroid formation and maintaining its physiological function. In thyroid cancer, NKX2-1 expression decreases in parallel with declined differentiation. However, the molecular pathways and mechanisms connecting NKX2-1 to thyroid cancer phenotypes are largely unknown. This study aimed to examine the effects of NKX2-1 re-expression on dedifferentiated thyroid cancer cell death and explore the underlying mechanisms. A human papillary thyroid carcinoma cell line lacking NKX2-1 expression was infected with an adenoviral vector containing
Nkx2-1
. Cell viability decreased after
Nkx2-1
transduction and apoptosis and necrosis were detected. Arginase 2 (
ARG2
), regulator of G protein signaling 4 (
RGS4
), and
RGS5
mRNA expression was greatly increased in
Nkx2-1
-transducted cells. After suppressing these genes by siRNA, cell death, apoptosis, and necrosis decreased in RGS4 knockdown cells. These findings demonstrated that cell death was induced via apoptosis and necrosis by NKX2-1 re-expression and involves RGS4.
Journal Article
Gcm2 regulates the maintenance of parathyroid cells in adult mice
Glial cells missing homolog 2 (GCM2), a zinc finger-type transcription factor, is essential for the development of parathyroid glands. It is considered to be a master regulator because the glands do not form when Gcm2 is deficient. Remarkably, Gcm2 expression is maintained throughout the fetal stage and after birth. Considering the Gcm2 function in embryonic stages, it is predicted that Gcm2 maintains parathyroid cell differentiation and survival in adults. However, there is a lack of research regarding the function of Gcm2 in adulthood. Therefore, we analyzed Gcm2 function in adult tamoxifen-inducible Gcm2 conditional knockout mice. One month after tamoxifen injection, Gcm2-knockout mice showed no significant difference in serum calcium, phosphate, and PTH levels and in the expressions of calcium-sensing receptor (Casr) and parathyroid hormone (Pth), whereas Ki-67 positive cells were decreased and terminal deoxynucleotidyl transferase (TdT) dUTP Nick-End Labeling (TUNEL) positive cell number did not change, as compared with those of controls. Seven months after tamoxifen injection, Gcm2-knockout mice showed shrinkage of the parathyroid glands and fewer parathyroid cells. A significant decrease was noted in Casr- and Pth-expressing cells and serum PTH and Ca levels, whereas serum phosphate levels increased, as compared with those of controls. All our results concluded that a reduction of Gcm2 expression leads to a reduction of parathyroid cell proliferation, an increase in cell death, and an attenuation of parathyroid function. Therefore, we indicate that Gcm2 plays a prominent role in adult parathyroid cell proliferation and maintenance.
Journal Article
Effect of matcha green tea on cognitive functions and sleep quality in older adults with cognitive decline: A randomized controlled study over 12 months
Lifestyle habits after middle age significantly impact the maintenance of cognitive function in older adults. Nutritional intake is closely related to lifestyle habits; therefore, nutrition is a pivotal factor in the prevention of dementia in the preclinical stages. Matcha green tea powder (matcha), which contains epigallocatechin gallate, theanine, and caffeine, has beneficial effects on cognitive function and mood. We conducted a randomized, double-blind, placebo-controlled clinical study over 12 months to examine the effect of matcha on cognitive function and sleep quality.
Ninety-nine participants, including 64 with subjective cognitive decline and 35 with mild cognitive impairment were randomized, with 49 receiving 2 g of matcha and 50 receiving a placebo daily. Participants were stratified based on two factors: age at baseline and APOE genotype. Changes in cognitive function and sleep quality were analyzed using a mixed-effects model.
Matcha consumption led to significant improvements in social acuity score (difference; -1.39, 95% confidence interval; -2.78, 0.002) (P = 0.028) as evaluated by the perception of facial emotions in cognitive function. The primary outcomes, that is, Montreal Cognitive Assessment and Alzheimer's Disease Cooperative Study Activity of Daily Living scores, showed no significant changes with matcha intervention. Meanwhile, Pittsburgh Sleep Quality Index scores indicated a trend toward improvement with a difference of 0.86 (95% confidence interval; -0.002, 1.71) (P = 0.088) between the groups in changes from baseline to 12 months.
The present study suggests regular consumption of matcha could improve emotional perception and sleep quality in older adults with mild cognitive decline. Given the widespread availability and cultural acceptance of matcha green tea, incorporating it into the daily routine may offer a simple yet effective strategy for cognitive enhancement and dementia prevention.
Journal Article
Disease-relevant upregulation of P2Y1 receptor in astrocytes enhances neuronal excitability via IGFBP2
Reactive astrocytes play a pivotal role in the pathogenesis of neurological diseases; however, their functional phenotype and the downstream molecules by which they modify disease pathogenesis remain unclear. Here, we genetically increase P2Y
1
receptor (P2Y1R) expression, which is upregulated in reactive astrocytes in several neurological diseases, in astrocytes of male mice to explore its function and the downstream molecule. This astrocyte-specific P2Y1R overexpression causes neuronal hyperexcitability by increasing both astrocytic and neuronal Ca
2+
signals. We identify insulin-like growth factor-binding protein 2 (IGFBP2) as a downstream molecule of P2Y1R in astrocytes; IGFBP2 acts as an excitatory signal to cause neuronal excitation. In neurological disease models of epilepsy and stroke, reactive astrocytes upregulate P2Y1R and increase IGFBP2. The present findings identify a mechanism underlying astrocyte-driven neuronal hyperexcitability, which is likely to be shared by several neurological disorders, providing insights that might be relevant for intervention in diverse neurological disorders.
Reactive astrocytes display aberrant Ca
2+
signals. Here, the authors show a link between P2Y1 receptor, a major regulator of the aberrant Ca
2+
signals, and IGFBP2 that may lead to neuronal hyperexcitability in neurological disorders.
Journal Article
Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study
Background
The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions.
Methods
To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC.
Results
We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores.
Conclusions
We show that Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.
Journal Article